• Journal of Distribution Science
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• v.6 no.2
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• pp.61-79
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• 2008

This paper studies on the influence of customers' perceived store crowding, shopping emotions, shopping value, and repatronage intention. The result is as follows. First, human crowding and spatial crowding influence two dimensions of shopping emotions: positive shopping emotions and negative shopping emotions. Especially human crowding influences positively on positive shopping emotions while spatial crowding influence positively on negative shopping emotions, which means that shoppers visiting stores feel negative shopping emotions toward spatial crowding, but that shoppers' crowding in stores can arose positive shopping emotions which intrigue interest and liveliness. Second, positive shopping emotions and negative shopping emotions influence two dimensions of shopping values: hedonic shopping value and utilitarian shopping value. Especially positive shopping emotion influences positively on utilitarian shopping value and hedonic shopping value while negative shopping emotion influence negatively on utilitarian shopping value and hedonic shopping value. Third, the result shows that shoppers' shopping values influences positively on repatronage intention.

• Culinary science and hospitality research
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• v.16 no.2
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• pp.110-135
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• 2010

The purpose of this study was to examine the effects of a mileage program on consumers' negative emotions and behavior when the program failed. To accomplish the purpose of this study, the failures in mileage programs were classified into three types(refusal, delay, reduced advantages) with two types of customers' commitment(affective and calculative), and an experimental design was used to verify the effect of customers' negative emotions on their negative behavior when the programs fail. A total of 600 university students in Seoul and Pusan areas were surveyed from 1st of Sept. to 30th in 2009. The result suggests that both consumers with affective commitment and those with calculative commitment felt negative emotions(regret, disappointment) when a mileage program failed. Consumers with affective commitment to a company are expected to give negative words of mouth to others. Also, consumers with calculative commitment to a company are expected to complain and give negative words of mouth to others.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.295-297
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• 2011

본 논문에서는 동적얼굴영상으로부터 감정인식을 위해 벡터 표현 보다는 직접적인 텐서 표현으로 특징들을 추출하는 텐서 기반 다선형 주성분분석(MPCA: Multilinear Principal Component Analysis) 기법을 사용한다. 사람 6가지의 얼굴 표정을 사용하는데 한 사람의 각 표정마다 5프레임으로 묶어서 텐서 형태로 취하여 특징을 추출하고 인식한다. 시스템의 성능 평가는 CNU 얼굴 감정인식 데이터베이스를 이용하여 특징점 개수와 성능척도에 따른 실험을 수행하여 제시된 방법의 유용성에 관해 살펴본다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.687-692
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• 2023

We analyze the accuracy of emotion analysis of natural language processing deep learning model and propose to use it for emotional content development. After looking at the outline of the GPT-3 model, about 6,000 pieces of dialogue data provided by Aihub were input to 9 emotion categories: 'joy', 'sadness', 'fear', 'anger', 'disgust', and 'surprise'. ', 'interest', 'boredom', and 'pain'. Performance evaluation was conducted using the evaluation indices of accuracy, precision, recall, and F1-score, which are evaluation methods for natural language processing models. As a result of the emotion analysis, the accuracy was over 91%, and in the case of precision, 'fear' and 'pain' showed low values. In the case of reproducibility, a low value was shown in negative emotions, and in the case of 'disgust' in particular, an error appeared due to the lack of data. In the case of previous studies, emotion analysis was mainly used only for polarity analysis divided into positive, negative, and neutral, and there was a limitation in that it was used only in the feedback stage due to its nature. We expand emotion analysis into 9 categories and suggest its use in the development of emotional content considering it from the planning stage. It is expected that more accurate results can be obtained if emotion analysis is performed by additionally collecting more diverse daily conversations through follow-up research.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.105-114
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• 2015

We propose a real time user interface that utilizes emotion recognition by physiological signals. To improve the problem that was low accuracy of emotion recognition through the traditional EEG(ElectroEncephaloGram), We developed a physiological signals-based emotion recognition system mixing relative power spectrum values of theta/alpha/beta/gamma EEG waves and autonomic nerve signal ratio of ECG (ElectroCardioGram). We propose both a data map and weight value modification algorithm to recognize six emotions of happy, fear, sad, joy, anger, and hatred. The datamap that stores the user-specific probability value is created and the algorithm updates the weighting to improve the accuracy of emotion recognition corresponding to each EEG channel. Also, as we compared the results of the EEG/ECG bio-singal complex data and single data consisting of EEG, the accuracy went up 23.77%. The proposed interface system with high accuracy will be utillized as a useful interface for controlling the game spaces and smart spaces.

• The Magazine of the IEIE
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• v.31 no.6
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• pp.52-62
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• 2004

음성은 인간과 인간의 의사소통 수단으로 가장 편리하게 사용되는 매체이다. 음성 중에는 여러 가지 정보가 포함되어 있지만 가장 기본적이고 중요한 것이 의미정보 즉 언어적 정보이다. 또한 음성에는 누가 말하고 있는가를 나타내는 개인성 정보, 말하는 사람의 감정을 전해주는 정서 정보 등이 있다.(중략)

• Iberoamérica
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• v.22 no.1
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• pp.73-109
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• 2020

In the tourism industry, emotional labor-related problems are highly likely because workers cannot avoid frequent face-to-face contacts with customers. Emotional labor, however, is not always recognized in the same pattern by every worker in similar situations. As can be see in this research, emotional labor appears to vary depending on individual characteristics and sociocultural and economic conditions. In fact, there are so many factors affecting emotional labor, and the level of influence concerning these factors can vary depending on when and where they are. Psychological and mental pains and stress from emotional labor depend on a number of factors. The expression of emotional labor, depending on age, length of employment and gender, shows a clear difference. In particular, for those who are planning to participate in international labor migration to the United States in the future, emotional labor was often not recognized as a serious problem or rather a positive social and cultural experience. In fact, there are various types of tourism workers in Oaxaca, and their experiences vary greatly depending on individual and economic and sociocultural environments. The emotional labor of the workers in the tourism sector of Oaxaca also has various forms depending on various conditions. Positive or negative perceptions and consequences of emotional labor vary depending on the circumstances of the individual and working conditions. In other words, socioeconomic conditions, individual personality or experiences, sociocultural characteristics, and autonomy in the workplace, have a significant impact. As a result, the forms and effects of emotional labor expressed and understood by tourism workers vary considerably and may vary depending on the situations. Considering these problems, the way emotional labor emerges is greatly influenced by sociocultural or personal factors as well as economic factors. In addition, rather than considering the existence and seriousness of emotional labor as given depending on the nature of a particular industry or work, it should also be recognized that the expression of emotional labor differ considerably from individual to individual. Also, the seriousness of problems caused by emotional labor can be diversified. In this sense, it is necessary to clearly understand the meanings of the dynamism, diversity and multidimensionality of emotional labor from a new perspective.

• Koreanishche Zeitschrift fur Deutsche Sprachwissenschaft
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• v.6
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• pp.23-55
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• 2002

이 논문의 목적은 수없이 많은 독일어 동사들 가운데 사람의 심리-감정을 표현하는 동사, 소위 감정동사(Emotionsverben: Verben der Gefuhlsbewegung)를 하나의 어휘-의미장(lexikalisch-semantisches Feld)으로 보고 이들의 통사구조 및 의미구조를 파악하여 결합가 모형화 하는 것이다. 우리는 감정동사의 통사 구조 및 의미구조를 기술하기 위해 동사 중심의 결합가 이론과 격이론을 이론적$\cdot$방법론적 토대로 삼았다. 또한 우리는 감정동사를 보충어의 수와 형태에 따라 크게 세 가지 그룹, 즉 2개의 보충어를 갖는 그룹 I(이 그룹에 속하는 동사들은 무생물(사물)을 주어로 갖는다)과 그룹 II(이 그룹에 속하는 동사들은 유생물(사람)을 주어로 갖는다) 그리고 3개의 보충어를 갖는 그룹 III(사람과 사람간의 관계를 나타낸다)으로 구분하였다. 예증을 위해 개별 동사에 대해 용례를 제시했다. 2개의 보충어를 갖는 그룹 II를 보충어의 수의성 여부에 따라 하위 분류했다. 보충어의 형태는 명사구(Sn, Sd, Sa, Sa)와 전치사구(pS)에 한정했으며 - 지면관계상 개별 동사의 예문으로 제시하진 않았지만 - 문장형태의 보충어, 예를 들어 dass-문장(Nsdass)과 부정사문(Inf)도 고려하여 통사적 문형(syntaktisches Satzmodell)과 의미적문형(semantisches Satzmodell)에서 제시하였다. 결국 이 논문은 독일어를 배우는 이들에게 독일어 동사의 통사구조 및 의미구조를 보다 쉽게 설명할 수 있는 하나의 방법론을 제시함은 물론, 나아가서는 결합가 사전에서 동사 내항 기술을 위한 기본적인 토대를 제공할 것이다

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.519-525
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• 2023

The human brain constantly emits electrical impulses, which is called brain waves, and brain waves can be defined as the electrical activity of the brain generated by the flow of ions generated by the biochemical interaction of brain cells. There is a study that emotion is one of the factors that can cause stress. Brain waves are the most used in the study of emotions. This paper is a study on whether emotions affect stress, and showed two images of fear and joy to four experimenters and divided them into three stages before, during, and after watching. As a measurement tool, brain waves at the positions of Fp1 and Fp2 were measured using the NeuroBrain System, a system that can automate brain wave measurement, analysis, brain wave reinforcement, and suppression training with remote control. After obtaining the brain wave data for each emotion, the average value was calculated and the study was conducted. As for the frequency related to stress, the values of Alpha and SMR, Low Beta, and High Beta were analyzed. Brainwave analysis affects stress depending on the emotional state, and "fear" emotions cause anxiety by raising Beta levels, resulting in higher Mind Stress levels, while "joy" emotions lower Beta levels, resulting in a significant drop in Mind Stress.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.832-837
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• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.